2.1 Low
CVSS2
Attack Vector
LOCAL
Attack Complexity
LOW
Authentication
NONE
Confidentiality Impact
NONE
Integrity Impact
NONE
Availability Impact
PARTIAL
AV:L/AC:L/Au:N/C:N/I:N/A:P
5.5 Medium
CVSS3
Attack Vector
LOCAL
Attack Complexity
LOW
Privileges Required
LOW
User Interaction
NONE
Scope
UNCHANGED
Confidentiality Impact
NONE
Integrity Impact
NONE
Availability Impact
HIGH
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
0.0004 Low
EPSS
Percentile
12.8%
The implementation of tf.raw_ops.MaxPoolGradWithArgmax
is vulnerable to a division by 0:
import tensorflow as tf
input = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32)
grad = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32)
argmax = tf.constant([], shape=[0], dtype=tf.int64)
ksize = [1, 1, 1, 1]
strides = [1, 1, 1, 1]
tf.raw_ops.MaxPoolGradWithArgmax(
input=input, grad=grad, argmax=argmax, ksize=ksize, strides=strides,
padding='SAME', include_batch_in_index=False)
The implementation fails to validate that the batch dimension of the tensor is non-zero, before dividing by this quantity.
We have patched the issue in GitHub commit 376c352a37ce5a68b721406dc7e77ac4b6cf483d.
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team.
2.1 Low
CVSS2
Attack Vector
LOCAL
Attack Complexity
LOW
Authentication
NONE
Confidentiality Impact
NONE
Integrity Impact
NONE
Availability Impact
PARTIAL
AV:L/AC:L/Au:N/C:N/I:N/A:P
5.5 Medium
CVSS3
Attack Vector
LOCAL
Attack Complexity
LOW
Privileges Required
LOW
User Interaction
NONE
Scope
UNCHANGED
Confidentiality Impact
NONE
Integrity Impact
NONE
Availability Impact
HIGH
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
0.0004 Low
EPSS
Percentile
12.8%